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https://github.com/wassname/scikit-image.git
synced 2026-07-28 11:25:42 +08:00
added removal function
This commit is contained in:
Vighnesh Birodkar
@@ -15,26 +15,28 @@ using the Sobel filter to signify the importance of each pixel.
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"""
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from skimage import io, data
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from skimage import transform
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from skimage import filters
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from skimage import filters, color
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from matplotlib import pyplot as plt
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def nothing(img):
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return img
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img = data.coins()#io.imread('/home/vighnesh/images/seam_bw.png')
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out = transform.seam_carve(img, 'vertical', 80, energy_func=filters.sobel)
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#img = io.imread('/home/vighnesh/images/castle.jpg')
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#img = color.rgb2gray(img)
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img = data.camera()
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out = transform.seam_carve(img, 'vertical', 50, energy_func=filters.sobel)
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#out = transform.seam_carve(out, 'horizontal', 70, energy_func=filters.sobel)
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resized = transform.resize(img, out.shape)
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plt.title('Original Image')
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#io.imshow(img, plugin='matplotlib')
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io.imshow(img, plugin='matplotlib')
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plt.figure()
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plt.title('Resized Image')
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#plt.figure()
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#plt.title('Resized Image')
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#io.imshow(resized, plugin='matplotlib')
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plt.figure()
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plt.title('Resized using Seam-Carving')
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#io.imshow(out, plugin='matplotlib')
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io.imshow(out, plugin='matplotlib')
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#io.show()
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io.show()
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@@ -4,111 +4,7 @@ cimport numpy as cnp
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cdef cnp.double_t DBL_MAX = np.finfo(np.double).max
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cdef _find_seam_v(cnp.double_t[:, ::1] energy_img, cnp.int8_t[:, ::1] track_img,
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cnp.double_t[::1] current_cost, cnp.double_t[::1] prev_cost,
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Py_ssize_t cols):
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"""Find a single vertical seam in an image that will be removed.
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Parameters
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----------
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energy_img : (M, N) ndarray
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The energy image where a higher value signifies a pixel of more
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importance. Pixels with a lower value will be cropped first.
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track_img : (M, N) ndarray
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The image used to store the optimal decision made at each point while
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finding a minimum cost path. For each pixel it stores the offset that
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produced that least cost.
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current_cost : (N,) ndarray
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An array to store the current cost of the optimal path for each column
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in row currently being processed.
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prev_cost : (N,) ndarray
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An array to store the current cost of the optimal path for each column
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in row prior to the one being processed.
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cols : int
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The number of cols to process for seam carving. Columns with indices
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more than `cols` are ignored.
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Returns
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-------
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seam : (M, ) ndarray of int
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An array containing the index of the row of the pixel to be removed
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for each column in the image.
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Notes
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-----
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`track_img`, `current_cost` and `prev_cost` are passed as arguments to
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avoid memory allocation at each iteration of `_seam_carve_v`.
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"""
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cdef Py_ssize_t rows, row, col
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rows = energy_img.shape[0]
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cdef cnp.double_t tmp, min_cost
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cdef Py_ssize_t offset, idx, offset_clip
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cdef Py_ssize_t[::1] seam = np.zeros(rows, dtype=np.int)
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for idx in range(cols):
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prev_cost[idx] = energy_img[0, idx]
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for row in range(1, rows):
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for col in range(0, cols):
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min_cost = DBL_MAX
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for offset in range(-1, 2):
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idx = col + offset
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if idx > cols - 1 or idx < 0:
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continue
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if prev_cost[idx] < min_cost:
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min_cost = prev_cost[idx]
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track_img[row, col] = offset
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current_cost[col] = min_cost + energy_img[row, col]
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prev_cost[:] = current_cost
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seam[rows-1] = np.argmin(current_cost)
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for row in range(rows-2, -1, -1):
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col = seam[row + 1]
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offset = track_img[row, col]
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seam[row] = seam[row + 1] + offset
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return seam
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cdef remove_seam_v(cnp.double_t[:, :, ::1] img, Py_ssize_t[::1] seam,
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Py_ssize_t cols):
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""" Removes one horizontal seam from the image.
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The method modifies `img` so that all pixels to the right of the vertical
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seam are pushed one place left.
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image : (M, N, 3) ndarray
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Input image whose vertical seam is to be removed.
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seam : (M, ) ndarray
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An array use to store the index of the column in the seam for each row.
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cols : int
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Number of columns in the input image to process. Column indices more
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than `cols` are ingored.
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Notes
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-----
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`seam` is passed as an argument so that we don't have to reallocate it for
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each iteration in `_seam_carve_v`.
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"""
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cdef Py_ssize_t rows, row, col, idx
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rows = img.shape[0]
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for row in range(rows):
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for idx in range(seam[row], cols - 1):
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img[row, idx, :] = img[row, idx + 1, :]
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cdef _preprocess_image(cnp.double_t[:, ::1] energy_img,
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cdef _preprocess_image(cnp.double_t[:, :, ::1] energy_img,
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cnp.double_t[:, ::1] cumulative_img,
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cnp.int8_t[:, ::1] track_img,
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Py_ssize_t cols):
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@@ -118,7 +14,7 @@ cdef _preprocess_image(cnp.double_t[:, ::1] energy_img,
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cdef cnp.double_t min_cost = DBL_MAX
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for c in range(cols):
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cumulative_img[0, c] = energy_img[0, c]
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cumulative_img[0, c] = energy_img[0, c, 0]
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for r in range(1, rows):
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@@ -135,7 +31,7 @@ cdef _preprocess_image(cnp.double_t[:, ::1] energy_img,
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track_img[r, c] = offset
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#print "min_cost = ", min_cost
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cumulative_img[r,c] = min_cost + energy_img[r, c]
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cumulative_img[r,c] = min_cost + energy_img[r, c, 0]
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#print "-------Cumulative Image --------"
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#print np.array(cumulative_img)
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@@ -159,14 +55,29 @@ cdef cnp.uint8_t mark_seam(cnp.int8_t[:, ::1] track_img, Py_ssize_t start_index,
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current_seam_indices[row] = col
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if seam_map[row, col]:
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#print "---------- Seam conflict at ", row, col
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return 0
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for row in range(rows):
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col = current_seam_indices[row]
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seam_map[row, col] = 1
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return 1
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cdef remove_seam(cnp.double_t[:, :, ::1] img,
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cnp.uint8_t[:, ::1] seam_map, Py_ssize_t cols):
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cdef Py_ssize_t rows = img.shape[0]
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cdef Py_ssize_t channels = img.shape[2]
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cdef Py_ssize_t r, c, ch, shift
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for r in range(rows):
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shift = 0
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for c in range(cols):
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shift += seam_map[r, c]
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for ch in range(channels):
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img[r, c, ch] = img[r, c + shift, ch]
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def _seam_carve_v(img, iters, energy_func, extra_args , extra_kwargs, border):
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""" Carve vertical seams off an image.
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@@ -213,28 +124,52 @@ def _seam_carve_v(img, iters, energy_func, extra_args , extra_kwargs, border):
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cdef Py_ssize_t[::1] sorted_indices
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cdef cnp.uint8_t[:, ::1] seam_map = seam_map_obj
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cdef Py_ssize_t cols = img.shape[1]
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cdef Py_ssize_t rows = img.shape[0]
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cdef Py_ssize_t seams_left = iters
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cdef Py_ssize_t seams_removed
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cdef Py_ssize_t seam_idx
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cdef cnp.double_t[:, :, ::1] image = img
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cdef cnp.int8_t[:, ::1] track_img = np.zeros(img.shape[0:2], dtype=np.int8)
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cdef cnp.double_t[:, ::1] cumulative_img = np.zeros(img.shape[0:2], dtype=np.float)
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cdef cnp.double_t[:, ::1] energy_img
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cdef cnp.double_t[:, :, ::1] energy_img
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energy_img_obj = energy_func(np.squeeze(img))
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energy_img_obj = energy_func(np.squeeze(img))[:, :, np.newaxis]**2
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energy_img_obj = np.ascontiguousarray(energy_img_obj)
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energy_img = energy_img_obj
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energy_img_obj[:, 0:border] = DBL_MAX
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energy_img_obj[:, cols-border:cols] = DBL_MAX
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energy_img_obj[:, 0:border, 0] = DBL_MAX
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energy_img_obj[:, cols-border:cols, 0] = DBL_MAX
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energy_img_obj[rows-border:rows,:,0] = energy_img_obj[rows-2*border:rows-border,:,0]
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_preprocess_image(energy_img, cumulative_img, track_img, cols)
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last_row[...] = cumulative_img[-1, :]
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sorted_indices = np.argsort(last_row_obj)
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#print "Sorted Indices = ", np.array(sorted_indices)
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#print "First sorted Index = ", sorted_indices[0]
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#print "Last Row = ", np.array(energy_img[-1, :])
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#print np.array()
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seam_idx = 0
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from skimage import io
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io.imshow(seam_map_obj*255)
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io.show()
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return img[:, 0:cols]
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while seams_left > 0:
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#print "sorted indices", np.array(sorted_indices)[:10]
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#print "sorted array ", np.sort(last_row_obj)[:10]
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#print "Seam starting at : ", sorted_indices[seam_idx]
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if mark_seam(track_img, sorted_indices[seam_idx], seam_map):
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seams_left -= 1
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cols -= 1
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#print "Seam marked ", seam_idx
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seam_idx += 1
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continue
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else:
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print "Seams removed = ", seam_idx
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seam_idx = 0
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remove_seam(image, seam_map, cols)
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remove_seam(energy_img, seam_map, cols)
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seam_map[...] = 0
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_preprocess_image(energy_img, cumulative_img, track_img, cols)
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last_row[:cols] = cumulative_img[-1, :cols]
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sorted_indices = np.argsort(last_row_obj)
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#from skimage import io
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#io.imshow(seam_map_obj*255)
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#io.show()
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return img#[:, 0:cols]
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